India’s emergence as a global leader in AI-enabled healthcare is no longer a hypothesis; it is a demonstrable reality, reflected in the scale, coherence, and strategic intent of its digital public health architecture. Through interoperable platforms, policy-driven innovation, and pragmatic regulatory stewardship, the country has built an ecosystem that moves beyond isolated pilots and delivers impact at population scale, reaching hundreds of millions. As governments across the Global South seek models that reconcile technological sophistication with social equity, India now offers something both credible and rare: a working blueprint in which advanced AI and large-scale public health advance in lockstep.
At the Winter Dialogue on Responsible AI for Synergistic Excellence (RAISE), hosted by the NIMS Institute of Public Health and Governance at NIMS University Rajasthan, The Interview World spoke exclusively with Dr. Anunaya Jain, Global Technical Director, Digital and Data Analytics Hub at Jhpiego Corporation. In that exchange, he articulated how data sovereignty, regulatory clarity, and ethical system design are redefining the trajectory of digital health. He also demonstrated how responsible AI, strategic partnerships, and disciplined governance can convert technological potential into sustained, real-world health outcomes across diverse societies. What follows are the essential insights from that discussion.
Q: How do you assess India’s emerging global leadership in AI-driven healthcare, and what role is your organization playing in empowering these capabilities across the Global South through strategic partnerships?
A: India now stands out as a public-health programming leader. From the vantage point of artificial intelligence and digital health, its ecosystem has become markedly more receptive to innovation. This shift did not occur by chance. It reflects deliberate investments in digital public infrastructure, a strong commitment to interoperability as a governing principle, and regulatory frameworks that support, rather than constrain, responsible innovation. The result is a foundation that enables AI and advanced digital health solutions to operate at population scale.
What India offers the world goes beyond discrete technologies or policy documents. It offers a coherent way of working. The country has developed a disciplined approach to adopting innovation in public health, one that aligns architecture, governance, and delivery with real-world health outcomes. Having worked across multiple countries, I can say with confidence that this integrated, adoption-driven mindset is what most clearly distinguishes India. It is also what makes its experience so valuable to others.
From our perspective as an international NGO, our role is not to “empower” in any abstract sense. We do not confer power. Instead, we support sovereign governments as they design and implement public-health programs to achieve measurable health outcomes. That mission defines everything we do.
Whether in Southeast Asia or across Africa, our operating model remains consistent. We provide targeted technical expertise and hands-on assistance in response to what governments themselves identify as priorities. In doing so, we strengthen national capacity, accelerate effective implementation, and help translate strategy into impact. That is how we work, and that is where we add the greatest value.
Q: You noted that public health data models often over-represent last-mile populations while under-representing urban and affluent groups. What factors explain this data asymmetry?
A: Affluent, educated urban residents generally understand what consent means and how data can be misused. Consequently, they act on that knowledge. When a mobile application asks for permission to track activity across platforms, they routinely decline. In doing so, they protect their privacy, but they also remove themselves from the data ecosystems that increasingly train AI systems.
By contrast, most data that feeds public-health AI comes from those who rely on public health services. These are not the urban affluent. Nor are they, in most cases, socially protected or highly resourced populations. They are frontline and last-mile communities, the people who depend most heavily on public systems for care. As a result, they are disproportionately represented in the datasets and algorithms that now shape public-health decision-making, while people like us are not.
This imbalance raises an uncomfortable but necessary question: who bears responsibility for participating in these data systems? To date, many of us have felt insulated from the consequences of opting out. However, that insulation will not last. If we withhold our data, AI systems will not learn how to serve us. They will reflect only the populations that remain visible within them.
True data equity therefore requires more than protecting the most vulnerable. It also requires those with privilege, agency, and choice to participate. Until we acknowledge what we lose by withholding our data, and how that loss distorts the systems being built, we will not achieve fairness in how AI represents and serves society.
Q: In the context of data sovereignty, how critical is responsible AI to the healthcare ecosystem, and how is your organization institutionalizing this principle among policymakers, providers, and industry stakeholders?
A: Data sovereignty is not a rhetorical ideal for us; it is a governing principle. In India, that principle carries particular weight because health is a state subject. As a result, even the movement of data across state boundaries requires careful scrutiny, let alone across national borders. Therefore, whenever we support a government in building a digital health application or deploying a new innovation, we require that all data remain within the country. We always ensure that it resides on government-owned or government-approved servers.
At the same time, we apply a strict doctrine of data minimalism. We collect only what is necessary to deliver the intended health outcome. We do not harvest data simply because we can. In practice, excess data creates risk, not value, especially when there is no clear purpose or use case attached to it.
We accept, without ambiguity, that data sovereignty is now a permanent feature of the global health landscape. A fully borderless data future may be theoretically appealing, but it is neither necessary nor decisive for achieving strong health outcomes. Countries can, and should, deliver effective, AI-enabled public health systems while retaining control over their own data.
That conviction shapes how we advise our partners and how we design our own programs. We do not dilute sovereignty to pursue innovation. We treat sovereignty as the condition that makes sustainable, trusted innovation possible.
Q: What governance, regulatory, and design frameworks are required to embed trust and ethical intent into India’s AI-enabled healthcare ecosystem?
A: Three conditions ultimately determine whether AI can scale responsibly in public health.
First, political and regulatory will must exist. Without clear policy direction, credible regulatory guidance, and sustained political commitment, adoption will stall. In India, those conditions already take shape. Political backing remains strong, and the regulatory architecture continues to mature. The recent CDSCO notification classifying cancer-screening AI as a Class C medical device illustrates this progress. It sends a clear signal to the market: AI in health will operate within a serious, enforceable regulatory framework.
Second, the ecosystem must enable trust. Even the best technology fails if people do not believe in it. That work remains incomplete. Both the general population and the health workforce still need a better understanding of what AI does, how it works, where it performs well, and where its limits lie. Building that literacy is essential, because informed trust, not blind acceptance, drives sustainable adoption.
Third, users and beneficiaries must participate in building the technology itself. Too often, innovators design AI in isolation and then attempt to deploy it into complex social systems. That approach no longer works. We need to return to the core principles of digital development: bring users and beneficiaries into the room from the very beginning, and design with them rather than for them.
Beneath all of this sits a more fundamental constraint: financial viability. If AI systems cannot sustain themselves economically, they will never reach scale. Yet if the ecosystem continues to treat intellectual property as the primary source of financial return, progress will remain limited. We need alternative models of profitability and sustainability that reward impact, not just ownership. Only then will AI in public health become both scalable and enduring.
